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bud dormancy
Noémie Vimont, Mathieu Fouche, José Campoy, Meixuezi Tong, Mustapha Arkoun, Jean-Claude Yvin, Philip Wigge, Elisabeth Dirlewanger, Sandra
Cortijo, Bénédicte Wenden
To cite this version:
Noémie Vimont, Mathieu Fouche, José Campoy, Meixuezi Tong, Mustapha Arkoun, et al.. From bud
formation to flowering: transcriptomic state defines the cherry developmental phases of sweet cherry
bud dormancy. 2020. �hal-02503000�
From bud formation to flowering: transcriptomic state defines the cherry developmental phases 1
of sweet cherry bud dormancy 2
3
Noémie Vimont
1,2,3, Mathieu Fouché
1, José Antonio Campoy
4,5,6, Meixuezi Tong
3, Mustapha Arkoun
2, 4
Jean-Claude Yvin
2, Philip A. Wigge
7, Elisabeth Dirlewanger
1, Sandra Cortijo
3#, Bénédicte Wenden
1#5 6
1UMR 1332 BFP, INRA, Univ. Bordeaux, 33882 Villenave d’Ornon, Cedex France; 2Agro Innovation International - Centre Mondial
7
d'Innovation - Groupe Roullier, 35400 St Malo, France; 3The Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR,
8
United Kingdom; 4 Universidad Politécnica de Cartagena, Cartagena, Spain; 5 Universidad de Murcia, Murcia, Spain; 6Current address:
9
Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany; 7Leibniz-
10
Institute für Gemüse- und Zierpflanzenbau (IGZ), Plant Adaptation, Grossbeeren, Germany
11
#Corresponding authors: sandra.cortijo@slcu.cam.ac.uk; benedicte.wenden@inra.fr
12 13 14
SUMMARY 15 16
● Bud dormancy is a crucial stage in perennial trees and allows survival over winter to ensure 17
optimal flowering and fruit production. Recent work highlighted physiological and molecular 18
events occurring during bud dormancy in trees and we aimed to further explore the global 19
transcriptional changes happening throughout dormancy progression.
20
● Using next-generation sequencing and modelling, we conducted an in-depth transcriptomic 21
analysis for all stages of flower buds in sweet cherry (Prunus avium L.) cultivars displaying 22
contrasted stages of bud dormancy.
23
● We observed that buds in organogenesis, paradormancy, endodormancy and ecodormancy 24
stages are characterised by specific transcriptional states, associated with different pathways.
25
We further identified that endodormancy can be separated in several phases based on the 26
transcriptomic state. We also found that transcriptional profiles of just seven genes are enough 27
to predict the main cherry tree flower bud dormancy stages.
28
● Our results indicate that transcriptional changes happening during dormancy are robust and 29
conserved between different sweet cherry cultivars. Our work also sets the stage for the 30
development of a fast and cost effective diagnostic tool to molecularly define the flower bud 31
stages in cherry trees.
32 33
KEY WORDS: Transcriptomic, RNA sequencing, time course, Prunus avium L., prediction, seasonal 34
timing 35
36
INTRODUCTION 37
38
Temperate trees face a wide range of environmental conditions including highly contrasted 39
seasonal changes. Among the strategies to enhance survival under unfavourable climatic conditions, 40
bud dormancy is crucial for perennial plants since its progression over winter is determinant for 41
optimal growth, flowering and fruit production during the subsequent season. Bud dormancy has long 42
been compared to an unresponsive physiological phase, in which metabolic processes within the buds 43
are halted by cold temperature. However, several studies have shown that bud dormancy progression 44
can be affected in a complex way by temperature and photoperiod (Heide & Prestrud, 2005; Allona et 45
al., 2008; Olsen, 2010; Cooke et al., 2012; Maurya et al., 2018). Bud dormancy has traditionally been 46
separated into three main phases: (i) paradormancy, also named “summer dormancy” (Cline &
47
Deppong, 1999); (ii) endodormancy, mostly triggered by internal factors; and (iii) ecodormancy, 48
controlled by external factors (Lang et al., 1987; Considine & Considine, 2016). Progression through 49
endodormancy requires cold accumulation whereas warmer temperatures, i.e. heat accumulation, drive 50
the competence to resume growth over the ecodormancy phase. Dormancy is thus highly dependent 51
on external temperatures, and changes in seasonal timing of bud break and blooming have been 52
reported in relation with global warming. Notably, advances in bud break and blooming dates in spring 53
have been observed in the northern hemisphere, thus increasing the risk of late frost damages (Badeck 54
et al., 2004; Menzel et al., 2006; Vitasse et al., 2014; Fu et al., 2015; Bigler & Bugmann, 2018) while 55
insufficient cold accumulation during winter may lead to incomplete dormancy release associated with 56
bud break delay and low bud break rate (Erez, 2000; Atkinson et al., 2013). These phenological 57
changes directly impact the production of fruit crops, leading to large potential economic losses 58
(Snyder & de Melo-abreu, 2005). Consequently, it becomes urgent to acquire a better understanding 59
of bud responses to temperature stimuli in the context of climate change in order to tackle fruit losses 60
and anticipate future production changes.
61
In the recent years, an increasing number of studies have investigated the physiological and molecular 62
mechanisms of bud dormancy transitions in perennials using RNA sequencing technology, thereby 63
giving a new insight into potential pathways involved in dormancy. The results suggest that the 64
transitions between the three main bud dormancy phases (para-, endo- and eco- dormancy) are 65
mediated by pathways related to phytohormones (Zhong et al., 2013; Chao et al., 2017; Khalil-Ur- 66
Rehman et al., 2017; Zhang et al., 2018), carbohydrates (Min et al., 2017; Zhang et al., 2018), 67
temperature (Ueno et al., 2013; Paul et al., 2014), photoperiod (Lesur et al., 2015), reactive oxygen 68
species (Takemura et al., 2015; Zhu et al., 2015), water deprivation (Lesur et al., 2015), cold 69
acclimation and epigenetic regulation (Kumar et al., 2016). Owing to these studies, a better
70
understanding of bud dormancy has been established in different perennial species (see for example, 71
the recent reviews (Beauvieux et al., 2018; Lloret et al., 2018; Falavigna et al., 2019). However we 72
are still missing a fine-resolution temporal understanding of transcriptomic changes happening over 73
the entire bud development, from bud organogenesis to bud break.
74
Indeed, the small number of sampling dates in existing studies seems to be insufficient to capture all 75
the information about changes occurring throughout the dormancy cycle as it most likely corresponds 76
to a chain of biological events rather than an on/off mechanism. Many unresolved questions remain:
77
What are the fine-resolution dynamics of gene expression related to dormancy? Are specific sets of 78
genes associated with dormancy stages? Since the timing for the response to environmental cues is 79
cultivar-dependant (Campoy et al., 2011; Wenden et al., 2017), are transcriptomic profiles during 80
dormancy different in cultivars with contrasted flowering date?
81
To explore these mechanisms, we conducted a transcriptomic analysis of sweet cherry (Prunus 82
avium L.) flower buds from bud organogenesis until the end of bud dormancy using next-generation 83
sequencing. Sweet cherry is a perennial species highly sensitive to temperature (Heide, 2008) and we 84
focused on three sweet cherry cultivars displaying contrasted flowering dates and response to 85
environmental conditions. We carried out a fine-resolution time-course spanning the entire bud 86
development, from flower organogenesis in July to spring in the following year when flowering occurs, 87
encompassing para-, enco- and ecodormancy phases. Our results indicate that transcriptional changes 88
happening during dormancy are conserved between different sweet cherry cultivars, opening the way 89
to the identification of key factors involved in the progression through bud dormancy.
90 91
MATERIAL AND METHODS 92
93
Plant material 94
Branches and flower buds were collected from four different sweet cherry cultivars with contrasted 95
flowering dates: ‘Cristobalina’, ‘Garnet’, ‘Regina’ and ‘Fertard’, which display extra-early, early, late 96
and very late flowering dates, respectively. ‘Cristobalina’, ‘Garnet’, ‘Regina’ trees were grown in an 97
orchard located at the Fruit Experimental Unit of INRA in Bourran (South West of France, 44° 19′ 56′′
98
N, 0° 24′ 47′′ E), under the same agricultural practices. ‘Fertard’ trees were grown in a nearby orchard 99
at the Fruit Experimental Unit of INRA in Toulenne, near Bordeaux (48° 51′ 46′′ N, 2° 17′ 15′′ E).
100
During the first sampling season (2015/2016), ten or eleven dates spanning the entire period from 101
flower bud organogenesis (July 2015) to bud break (March 2016) were chosen for RNA sequencing 102
(Table S1; Fig. 1a), while bud tissues from ‘Fertard’ were sampled in 2015/2016 (12 dates) and 103
2017/2018 (7 dates) for validation by qRT-PCR (Table S1). For each date, flower buds were sampled
104
from different trees, each tree corresponding to a biological replicate. Upon harvesting, buds were 105
flash frozen in liquid nitrogen and stored at -80°C prior to performing RNA-seq.
106
107
Measurements of bud break and estimation of the dormancy release date 108
For the two sampling seasons, 2015/2016 and 2017/2018, three branches bearing floral buds were 109
randomly chosen fortnightly from ‘Cristobalina’, ‘Garnet’, ‘Regina’ and ‘Fertard’ trees, between 110
November and flowering time (March-April). Branches were incubated in water pots placed under 111
forcing conditions in a growth chamber (25°C, 16h light/ 8h dark, 60-70% humidity). The water was 112
replaced every 3-4 days. After ten days under forcing conditions, the total number of flower buds that 113
reached the BBCH stage 53 (Meier, 2001; Fadón et al., 2015) was recorded. The date of dormancy 114
release was estimated as the date when the percentage of buds at BBCH stage 53 was above 50% after 115
ten days under forcing conditions (Fig. 1a).
116 117
RNA extraction and library preparation 118
Fig 1 Dormancy status under environmental conditions and RNA-seq sampling dates
(a) Evaluation of bud break percentage under forcing conditions was carried out for three sweet cherry
cultivars displaying different flowering dates in ‘Cristobalina’, ‘Garnet’ and ‘Regina’ for the early, medium
and late cultivar, respectively. The coloured dotted line corresponds to the dormancy release date, estimated
at 50% of buds at BBCH stage 53 (Meier, 2001). (b) Pictures of the sweet cherry buds corresponding to the
different sampling dates. (c) Sampling time points for the transcriptomic analysis are represented by
coloured stars. Red for ‘Cristobalina, green for ‘Garnet’ and blue for ‘Regina’.
Total RNA was extracted from 50-60 mg of frozen and pulverised flower buds using RNeasy Plant 119
Mini kit (Qiagen) with minor modification: 1.5% PVP-40 was added in the extraction buffer RLT.
120
RNA quality was evaluated using Tapestation 4200 (Agilent Genomics). Library preparation was 121
performed on 1 μg of high quality RNA (RNA integrity number equivalent superior or equivalent to 122
8.5) using the TruSeq Stranded mRNA Library Prep Kit High Throughput (Illumina cat. no. RS-122- 123
2103) for ‘Cristobalina’, ‘Garnet’ and ‘Regina’ cultivars. DNA quality from libraries was evaluated 124
using Tapestation 4200. The libraries were sequenced on a NextSeq500 (Illumina), at the Sainsbury 125
Laboratory Cambridge University (SLCU), using paired-end sequencing of 75 bp in length.
126 127
Mapping and differential expression analysis 128
The raw reads obtained from the sequencing were analysed using several publicly available software 129
and in-house scripts. The quality of reads was assessed using FastQC 130
(www.bioinformatics.babraham.ac.uk/projects/fastqc/) and possible adaptor contaminations and low 131
quality trailing sequences were removed using Trimmomatic (Bolger et al., 2014). Trimmed reads 132
were mapped to the peach (Prunus persica (L) Batsch) reference genome v.2 (Verde et al., 2017) using 133
Tophat (Trapnell et al., 2009). Possible optical duplicates were removed using Picard tools 134
(https://github.com/broadinstitute/picard). The total number of mapped reads of each samples are 135
given in Table S2. For each gene, raw read counts and TPM (Transcripts Per Million) numbers were 136
calculated (Wagner, 2003).
137
We performed a differential expression analysis on data obtained from the ‘Garnet’ samples. First, 138
data were filtered by removing lowly expressed genes (average read count < 3), genes not expressed 139
in most samples (read counts = 0 in more than 75% of the samples) and genes presenting little ratio 140
change (coefficient of variation < 0.3). Then, differentially expressed genes (DEGs) between bud 141
stages (organogenesis – 6 biological replicates, paradormancy – 3 biological replicates, endodormancy 142
– 10 biological replicates, dormancy breaking – 6 biological replicates, ecodormancy – 6 biological 143
replicates, see Table S1) were assessed using DEseq2 R Bioconductor package (Love et al., 2014), in 144
the statistical software R (R Core Team 2018), on filtered data. Genes with an adjusted p-value (padj) 145
< 0.05 were assigned as DEGs (Table S3). To enable researchers to access this resource, we have 146
created a graphical web interface to allow easy visualisation of transcriptional profiles throughout 147
flower bud dormancy in the three cultivars for genes of interest (bwenden.shinyapps.io/DorPatterns/).
148 149
Principal component analyses and hierarchical clustering 150
Distances between the DEGs expression patterns over the time course were calculated based on 151
Pearson’s correlation on ‘Garnet’ TPM values. We applied a hierarchical clustering analysis on the
152
distance matrix to define ten clusters (Table S3). For expression patterns representation, we normalized 153
the data using z-score for each gene:
154
𝑧 𝑠𝑐𝑜𝑟𝑒 = (𝑇𝑃𝑀 − 𝑚𝑒𝑎𝑛 ) 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 155
where TPM
ijis the TPM value of the gene i in the sample j, mean
iand standard deviation
iare the mean 156
and standard deviation of the TPM values for the gene i over all samples.
157
Principal component analyses (PCA) were performed on TPM values from different datasets using the 158
prcomp function from R.
159
For each cluster, using data for ‘Garnet’, ‘Regina’ and ‘Cristobalina’, mean expression pattern was 160
calculated as the mean z-score value for all genes belonging to the cluster. We then calculated the 161
Pearson’s correlation between the z-score values for each gene and the mean z-score for each cluster.
162
We defined the marker genes as genes with the highest correlation values, i.e. genes that represent the 163
best the average pattern of the clusters. Keeping in mind that the marker genes should be easy to 164
handle, we then selected the optimal marker genes displaying high expression levels while not 165
belonging to extended protein families.
166 167
Motif and transcription factor targets enrichment analysis 168
We performed enrichment analysis on the DEG in the different clusters for transcription factor targets 169
genes and target motifs.
170
Motif discovery on the DEG set was performed using Find Individual Motif occurrences (FIMO) 171
(Grant et al., 2011). Motif list available for peach was obtained from PlantTFDB 4.0 (Jin et al., 2017).
172
To calculate the overrepresentation of motifs, DEGs were grouped by motif (grouping several genes 173
and transcripts in which the motif was found). Overrepresentation of motifs was performed using 174
hypergeometric tests using Hypergeometric {stats} available in R. Comparison was performed for the 175
number of appearances of a motif in one cluster against the number of appearances on the overall set 176
of DEG. As multiple testing implies the increment of false positives, p-values obtained were corrected 177
using False Discovery Rate (Benjamini & Hochberg, 1995) correction method using p.adjust{stats}
178
function available in R.
179
A list of predicted regulation between transcription factors and target genes is available for peach in 180
PlantTFDB (Jin et al., 2017). We collected the list and used it to analyse the overrepresentation of 181
genes targeted by TF, using Hypergeometric {stats} available in R, comparing the number of 182
appearances of a gene controlled by one TF in one cluster against the number of appearances on the 183
overall set of DEG. p-values obtained were corrected using a false discovery rate as described above.
184
Predicted gene homology to Arabidopsis thaliana and functions were retrieved from the data files
185
available for Prunus persica (GDR, 186
https://www.rosaceae.org/species/prunus_persica/genome_v2.0.a1).
187 188
GO enrichment analysis 189
The list for the gene ontology (GO) terms was retrieved from the database resource PlantRegMap (Jin 190
et al., 2017). Using the topGO package (Alexa & Rahnenführer, 2018), we performed an enrichment 191
analysis on GO terms for biological processes, cellular components and molecular functions based on 192
a classic Fisher algorithm. Enriched GO terms were filtered with a p-value < 0.005 and the ten GO 193
terms with the lowest p-value were selected for representation.
194 195
Marker genes qRT-PCR analyses 196
cDNA was synthetised from 1µg of total RNA using the iscript Reverse Transcriptase Kit (Bio-rad 197
Cat no 1708891) in 20 µl of final volume. 2 µL of cDNA diluted to a third was used to perform the 198
qPCR in a 20 µL total reaction volume. qPCRs were performed using a Roche LightCycler 480. Three 199
biological replicates for each sample were performed. Primers used in this study for qPCR are:
200
PavCSLG3 F: CCAACCAACAAAGTTGACGA , R: CAACTCCCCCAAAAAGATGA ; PavMEE9:
201
F: CTGCAGCTGAACTGGAACAG , R: ACTCATCCATGGCACTCTCC ; PavSRP:
202
F: ACAGGATCTGGAAAGCCAAG , R: AGGGTGGCTCTGAAACACAG ; PavTCX2:
203
F: CTTCCCACAACGCCTTTACG , R: GGCTATGTCTCTCAAACTTGGA ; PavGH127:
204
F: GCCATTGGTTGTAGGGTTTG , R: ATCCCATTCAGCATTCGTTC; PavUDP-GALT1 205
F: CAATGTTGCTGGAAACCTCA , R: GTTATTCCACATCCGACAGC ; PavPP2C 206
F: CTGTGCCTGAAGTGACACAGA , R: CTGCACTGCTTCTTGATTTG ; PavRPII 207
F: TGAAGCATACACCTATGATGATGAAG , R: CTTTGACAGCACCAGTAGATTCC ; PavEF1 208
F: CCCTTCGACTTCCACTTCAG , R: CACAAGCATACCAGGCTTCA . Primers were tested for non-specific 209
products previously by separation on 1.5% agarose gel electrophoresis and by sequencing each 210
amplicon. Realtime data were analyzed using custom R scripts. Expression was estimated for each 211
gene in each sample using a cDNA standard curve. For the visualization of the marker genes’ relative 212
expression, we normalized the qRT-PCR results for each marker gene by the average qRT-PCR data 213
for the reference genes PavRPII and PavEF1.
214 215
Bud stage predictive modelling 216
In order to predict the bud stage based on the marker genes transcriptomic data, we used TPM values 217
for the marker genes to train a multinomial logistic regression. First, all samples were projected into a 218
2-dimensional space using PCA, to transform potentially correlated data to an orthogonal space. The
219
new coordinates were used to train and test the model to predict the five bud stage categories, using 220
the LogisticRegressionCV function from the scikit-learn Python package (Pedregosa et al., 2011). The 221
model was 4-fold cross-validated to ensure the robustness of the coefficients and to reduce overfitting.
222
The model accuracy was calculated as the percentage of correct predicted stages in the RNA-seq 223
testing set. In addition, we tested the model on qRT-PCR data for ‘Fertard’ samples. For the modelling 224
purposes, expression data for the seven marker genes were normalized by the expression 225
corresponding to the October sample. We chose the date of October as the reference because it 226
corresponds to the beginning of dormancy and it was available for all cultivars. For each date, the 227
October-normalized expression values of the seven marker genes were projected in the PCA 2- 228
dimension plan calculated for the RNA-seq data and they were tested against the model trained on 229
‘Cristobalina’, ‘Garnet’ and ‘Regina’ RNA-seq data.
230 231
RESULTS 232
233
Transcriptome accurately captures the dormancy state 234
In order to define transcriptional changes happening over the sweet cherry flower bud 235
development, we performed a transcriptomic-wide analysis using next-generation sequencing from 236
bud organogenesis to flowering. According to bud break percentage (Fig. 1a), morphological 237
observations (Fig. 1b), average temperatures (Fig. S1) and descriptions from Lang et al., (1987), we 238
assigned five main stages to the early flowering cultivar ‘Garnet’ flower buds samples (Fig. 1b): i) 239
flower bud organogenesis occurs in July and August, ii) paradormancy corresponds to the period of 240
growth cessation in September, iii) during the endodormancy phase, initiated in October, buds are 241
unresponsive to forcing conditions therefore the increasing bud break percentage under forcing 242
conditions suggests that endodormancy was released on January 29th, 2016, thus corresponding to iv) 243
dormancy breaking, and v) ecodormancy starting from the estimated dormancy release date until 244
flowering.
245
We identified 6,683 genes that are differentially expressed (DEGs) between the defined bud 246
stages for the sweet cherry cultivar ‘Garnet’ (Table S3). When projected into a two-dimensional space 247
(Principal Component Analysis, PCA), data for these DEGs show that transcriptomes of samples 248
harvested at a given date are projected together (Fig. 2), showing the high quality of the biological 249
replicates and that different trees are in a very similar transcriptional state at the same date. Very 250
interestingly, we also observe that flower bud states are clearly separated on the PCA, with the 251
exception of organogenesis and paradormancy, which are projected together (Fig. 2). The first 252
dimension of the analysis (PC1) explains 41,63% of the variance and clearly represents the strength of
253
bud dormancy where samples on the right of the axis are in endodormancy or dormancy breaking 254
stages. The second dimension of the analysis (PC2) explains 20.24% of the variance and distinguishes 255
two main phases of the bud development: before and after dormancy breaking. We obtain very similar 256
results when performing the PCA on all genes (Fig. S2). These results indicate that the transcriptional 257
state of DEGs accurately captures the dormancy state of flower buds.
258
259
Bud stage-dependent transcriptional activation and repression are associated with different 260
pathways 261
We further investigated whether specific genes or signalling pathways could be associated with 262
the different flower bud stages. Indeed, the expression of genes grouped in ten clusters clearly shows 263
distinct expression profiles throughout the bud development (Fig. 3). Overall, three main types of 264
clusters can be discriminated: the ones with a maximum expression level during organogenesis and 265
paradormancy (cluster 1: 1,549 genes; cluster 2: 70 genes; cluster 3: 113 genes; cluster 4: 884 genes 266
and cluster 10: 739 genes, Fig. 3), the clusters with a maximum expression level during endodormancy 267
and around the time of dormancy breaking (cluster 5: 156 genes; cluster 6: 989 genes ; cluster 7: 648 268
genes and cluster 8: 612 genes, Fig. 3), and finally the clusters with a maximum expression level during 269
ecodormancy (cluster 9: 924 genes and cluster 10, Fig. 3). This result shows that different groups of 270
genes are associated with these three main flower bud phases. Interestingly, we also observed that, 271
during the endodormancy phase, some genes are expressed in October and November then repressed 272
Fig 2 Separation of samples by dormancy stage using differentially expressed genes
The principal component analysis was conducted on the TPM (transcripts per millions reads) values for the
differentially expressed genes in the cultivar ‘Garnet’ flower buds, sampled on three trees between July and
March.
in December (cluster 4, Fig. 3), whereas another group of genes is expressed in December (clusters 8, 273
5, 6 and 7, Fig. 3) therefore separating endodormancy in two distinct phases.
274
In order to explore the functions and pathways associated with the gene clusters, we performed 275
a GO enrichment analysis (Fig. 4, Fig. S3). GO terms associated with the response to stress as well as 276
biotic and abiotic stimuli were enriched in the clusters 2, 3 and 4, with genes mainly expressed during 277
organogenesis and paradormancy. During endodormancy (cluster 5), an enrichment for genes involved 278
in response to nitrate and nitrogen compounds was spotted. On the opposite, at the end of the 279
endodormancy phase (cluster 6, 7 and 8), we highlighted different enrichments in GO terms linked to 280
basic metabolisms such as nucleic acid metabolic processes or DNA replication but also to response 281
to alcohol and abscisic acid. Finally, during ecodormancy, genes in cluster 9 and 10 are enriched in 282
functions associated with transport, cell wall biogenesis as well as oxidation-reduction processes (Fig.
283
Fig 3 Clusters of expression patterns for differentially expressed genes in the sweet cherry cultivar
‘Garnet’
Heatmap for ‘Garnet’ differentially expressed genes during bud development. Each column corresponds to the gene expression for flower buds from one single tree at a given date. Clusters are ordered based on the chronology of the expression peak (from earliest – July, 1-dark green cluster – to latest – March, 9 and 10).
Expression values were normalized and z-scores are represented here.
4, Fig. S3). These results show that different functions and pathways are specific to flower bud 284
development stages.
285
Fig 4 Enrichments in gene ontology terms for biological processes and average expression patterns in the different clusters in the sweet cherry cultivar ‘Garnet’
(a) Using the topGO package (Alexa & Rahnenführer, 2018), we performed an enrichment analysis on GO
terms for biological processes based on a classic Fisher algorithm. Enriched GO terms with the lowest p-
value were selected for representation. Dot size represent the number of genes belonging to the clusters
associated with the GO term. (b) Average z-score values for each cluster. The coloured dotted line
corresponds to the estimated date of dormancy release.
Table 1. Enrichment in transcription factor targets in the different clusters 286
Based on the gene regulation information available for peach in PlantTFDB (Jin et al., 2017), overrepresentation of genes targeted by transcription factors was 287
performed using hypergeometric tests. p-values obtained were corrected using a false discovery rate: (***): adj. p-value < 0.001; (**): adj. p-value < 0.01; (*):
288
adj. p-value < 0.05.
289
Gene Name gene id Transcription Factor Cluster
Predicted TF
family Arabidopsis
homologous Predicted function Enrichment
p value
Enrichment adjusted p value
1 - Dark green
PavMYB63 Prupe.4G136300 1 - Dark green MYB AT1G79180 Myb-related protein 2,1E-05 6,7E-03 (**)
PavMYB93 Prupe.6G188300 1 - Dark green MYB AT1G34670 Myb-related protein 9,0E-04 3,2E-02 (*)
PavMYB40 Prupe.3G299000 8 - royal blue MYB AT5G14340 Myb-related protein 2,7E-04 1,7E-02 (*)
PavMYB17 Prupe.2G164300 - MYB AT3G61250 Myb-related protein 6,8E-05 7,2E-03 (**)
PavMYB94 Prupe.5G193200 - MYB AT3G47600 Myb-related protein 9,0E-05 7,2E-03 (**)
PavMYB60 Prupe.7G018400 - MYB AT1G08810 Myb-related protein 7,0E-05 7,2E-03 (**)
PavMYB61 Prupe.6G303300 - MYB AT1G09540 Myb-related protein 4,0E-04 2,1E-02 (*)
PavMYB3 Prupe.1G551400 - MYB AT1G22640 Myb-related protein 6,0E-04 2,8E-02 (*)
PavMYB67 Prupe.4G126900 - MYB AT3G12720 Myb-related protein 7,8E-04 3,1E-02 (*)
2 - grey Prupe.1G122800 - CAMTA AT4G16150 Calmodulin-binding transcription activator 3,1E-05 8,0E-03 (**)
3 - pink
PavWRKY40 Prupe.3G098100 3 - pink WRKY AT1G80840 WRKY transcription factor 8,4E-05 1,2E-02 (*)
Prupe.1G122800 - CAMTA AT4G16150 Calmodulin-binding transcription activator 4,9E-09 1,4E-06 (***)
PavWRKY11 Prupe.1G459100 - WRKY AT4G31550 WRKY transcription factor 4,7E-04 4,5E-02 (*)
5 - brown PavCBF4 Prupe.2G289500 - ERF AT5G51990 Dehydration-responsive element-binding protein 2,0E-04 5,7E-02 6 - orange
PavERF110 Prupe.6G165700 8 - royal blue ERF AT5G50080 Ethylene-responsive transcription factor 3,1E-04 5,2E-02 PavRVE8 Prupe.6G242700 8 - royal blue MYB AT3G09600 Homeodomain-like superfamily protein RVE8 4,3E-04 5,2E-02
PavRAP2.12 Prupe.3G032300 ERF AT1G53910 Ethylene-responsive transcription factor 4,9E-04 5,2E-02
8 - royal blue
PavRVE1 Prupe.3G014900 6 - orange MYB AT5G17300 Homeodomain-like superfamily protein RVE1 1,0E-03 3,6E-02 (*)
PavABI5 Prupe.7G112200 7 - red bZIP AT2G36270 ABSCISIC ACID-INSENSITIVE 5 6,6E-05 7,0E-03 (**)
PavABF2 Prupe.1G434500 8 - royal blue bZIP AT1G45249 abscisic acid responsive elements-binding factor 2,4E-06 7,5E-04 (***)
PavAREB3 Prupe.2G056800 - bZIP AT3G56850 ABA-responsive element binding protein 1,4E-05 2,2E-03 (**)
PavPIL5 Prupe.8G209100 - bHLH AT2G20180 phytochrome interacting factor 3-like 5 2,3E-04 1,9E-02 (*)
PavbZIP16 Prupe.5G027000 - bZIP AT2G35530 basic region/leucine zipper transcription factor 4,3E-04 2,7E-02 (*)
PavSPT Prupe.7G131400 - bHLH AT4G36930 Transcription factor SPATULA 5,6E-04 3,0E-02 (*)
PavBPE Prupe.1G263800 - bHLH AT1G59640 Transcription factor BPE 1,0E-03 3,6E-02 (*)
PavPIF4 Prupe.3G179800 - bHLH AT2G43010 phytochrome interacting factor 4 9,5E-04 3,6E-02 (*)
PavGBF3 Prupe.2G182800 - bZIP AT2G46270 G-box binding factor 3 1,1E-03 3,6E-02 (*)
9 - purple PavWRKY50 Prupe.1G407500 - WRKY AT5G26170 WRKY transcription factor 1,1E-04 1,8E-02 (*)
PavWRKY1 Prupe.3G202000 - WRKY AT2G04880 WRKY transcription factor 5,8E-05 1,8E-02 (*)
10 - yellow PavMYB14 Prupe.1G039200 5 - brown MYB AT2G31180 Myb-related protein 1,6E-04 3,9E-02 (*)
PavNAC70 Prupe.8G002500 - NAC AT4G10350 NAC domain containing protein 2,4E-04 3,9E-02 (*)
